Ellegaardmccoy1624

Fitting of curvilinear regressions to small data samples allows expeditious assessment of child growth in a number of characteristics when situations change rapidly, resources are limited and access to children is restricted.

The Brazilian Family Health Strategy (FHS) is strongly associated with better health system performance, but there are no nationally-representative data examining individual-level primary care experiences in the country. Here, we examine reports of primary care experiences among adults with different forms of healthcare coverage (FHS, "traditional" public health posts, and private health plans).

Data are from the 2019 National Health Survey that included a shortened version of the Primary Care Assessment Tool (PCAT). PCAT questions were administered to a subsample of randomly-selected adults who had a doctor visit within the past 6 months and sought care in a primary care setting (9677 respondents). We used linear regression to examine the association between type of healthcare coverage and PCAT scores adjusted for sex, age, socioeconomic status, health status, geographic region and state of residence.

Primary care experiences in the sample of Brazilians who had a doctor visit 6 months prior to the survey averaged a modest PCAT score of 57 out of 100. Regression models show that users of the FHS had superior primary care experiences, but with large variations across Brazilian regions and states. Individuals selected to respond to the PCAT questions were more likely to be female, older, and poorer, and to be in worse health than the general population.

Brazil's FHS is associated with modest, but higher-reported primary care experiences than both traditional public health posts and those who have a private health plan. Future iterations of the PCAT module could enhance generalizability by including individuals who had a doctor visit in the past 12 (instead of 6) months.

Brazil's FHS is associated with modest, but higher-reported primary care experiences than both traditional public health posts and those who have a private health plan. PR171 Future iterations of the PCAT module could enhance generalizability by including individuals who had a doctor visit in the past 12 (instead of 6) months.The COVID-19 pandemic has caused over 500 million cases and over six million deaths globally. From these numbers, over 12 million cases and over 250 thousand deaths have occurred on the African continent as of May 2022. Prevention and surveillance remains the cornerstone of interventions to halt the further spread of COVID-19. Google Health Trends (GHT), a free Internet tool, may be valuable to help anticipate outbreaks, identify disease hotspots, or understand the patterns of disease surveillance. We collected COVID-19 case and death incidence for 54 African countries and obtained averages for four, five-month study periods in 2020-2021. Average case and death incidences were calculated during these four time periods to measure disease severity. We used GHT to characterize COVID-19 incidence across Africa, collecting numbers of searches from GHT related to COVID-19 using four terms 'coronavirus', 'coronavirus symptoms', 'COVID19', and 'pandemic'. The terms were related to weekly COVID-19 case incidences for might be useful in specific situations, such as when countries have significant levels of infection with low variability. Future studies might assess the algorithm in different epidemic contexts.Avian influenza virus (AIV) represents a major concern with productive implications in poultry systems but it is also a zoonotic agent that possesses an intrinsic pandemic risk. AIV is an enveloped, negative-sense and single-stranded RNA virus with a segmented genome. The eight genomic segments, comprising the whole genome, encode for eleven proteins. Within these proteins, Hemagglutinin (HA) and Neuraminidase (NA) are the most relevant for studies of evolution and pathogenesis considering their role in viral replication, and have also been used for classification purposes. Migratory birds are the main hosts and play a pivotal role in viral evolution and dissemination due to their migratory routes that comprise large regions worldwide. Altogether, viral and reservoir factors contribute to the emergence of avian influenza viruses with novel features and pathogenic potentials. The study aimed to conduct surveillance of AIVs in wild birds from Peru. A multi-site screening of feces of migratory birds was performed to isolate viruses and to characterize the whole genome sequences, especially the genes coding for HA and NA proteins. Four-hundred-twenty-one (421) fecal samples, collected between March 2019 and March 2020 in Lima, were obtained from 21 species of wild birds. From these, we isolated five AIV from whimbrel, kelp gull, Franklin's gulls and Mallard, which were of low pathogenicity, including four subtypes as H6N8, H13N6, H6N2 and H2N6. Genetic analysis of HA and NA genes revealed novel features in these viruses and phylogenetic analysis exhibited a close relationship with those identified in North America (US and Canada). Furthermore, H2N6 isolate presented a NA sequence with higher genetic relationship to Chilean isolates. These results highlight that the geographical factor is of major relevance in the evolution of AIV, suggesting that AIV circulating in Peru might represent a new site for the emergence of reassortant AIVs.Most existing studies on land consumption have used a reactive approach to assess the phenomenon. However, for evidence-based policies, an initiative-taking forecast has been touted to be more appropriate. This study, therefore, assessed current trends and efficiency of land consumption in the Greater Accra Region from 1987 to 2017, and predicted a 30-year future land consumption in a "business-as-usual" scenario. The study adopted maximum likelihood image classification techniques and "combinatorial or" to model land cover change for Greater Accra from 1987 to 2017 while the UN-Habitat land efficiency index was employed to model efficiency of land consumption. In addition, Leo-Breiman Forest based regression, was used to model a future land cover by using the 30 years land cover change as a dependant variable and a series of natural and anthropogenic factors as independent variables. Results showed that artificial surfaces increased from 4.2% to 33.1%, with an annual growth rate of 22.1% in 30 years. Land consumption was highly inefficient as only 4.2% of the region had a good proportion of population per land area. Factors which influenced artificial surface growth were population, distance from water bodies, poverty index, distance from sacred groves, proportion of agriculture population with a small margin of influence from soil and geology type. Landscape prediction showed that artificial surfaces will increase to 92.6% as more places are coated with concrete. The high rate of land inefficiency provides an opportunity for re-zoning by the Land Use and Spatial Planning Authority of Ghana to accommodate the growing population.De novo variants (DNVs) with deleterious effects have proved informative in identifying risk genes for early-onset diseases such as congenital heart disease (CHD). A number of statistical methods have been proposed for family-based studies or case/control studies to identify risk genes by screening genes with more DNVs than expected by chance in Whole Exome Sequencing (WES) studies. However, the statistical power is still limited for cohorts with thousands of subjects. Under the hypothesis that connected genes in protein-protein interaction (PPI) networks are more likely to share similar disease association status, we developed a Markov Random Field model that can leverage information from publicly available PPI databases to increase power in identifying risk genes. We identified 46 candidate genes with at least 1 DNV in the CHD study cohort, including 18 known human CHD genes and 35 highly expressed genes in mouse developing heart. Our results may shed new insight on the shared protein functionality among risk genes for CHD.Outbreaks of H5-type highly pathogenic avian influenza (HPAI) in poultry have been reported in various parts of the world. To respond to these continuous threats, numerous surveillance programs have been applied to poultry raising facilities as well as wild birds. In Korea, a surveillance program was developed aimed at providing a preemptive response to possible outbreaks at poultry farms. The purpose of this study is to comprehensively present the risks of HPAI evaluated by this program in relation to actual outbreak farms during the epidemic of 2020/2021. A deep learning-based risk assessment program was trained based on the pattern of livestock vehicles visiting poultry farms and HPAI outbreaks to calculate the risk of HPAI for farms linked by the movement of livestock vehicles (such farms are termed "epidemiologically linked farms"). A total of 7,984 risk assessments were conducted, and the results were categorized into four groups. The proportion of the highest risk level was greater in duck farms (13.6% measures can be implemented for each epidemiologically linked farm. The use of this risk assessment program would be a good example of information-based surveillance and support decision-making for controlling animal diseases.

Transcription factor (TF) regulates the transcription of DNA to messenger RNA by binding to upstream sequence motifs. Identifying the locations of known motifs in whole genomes is computationally intensive.

This study presents a computational tool, named "Grit", for screening TF-binding sites (TFBS) by coordinating transcription factors to their promoter sequences in orthologous genes. This tool employs a newly developed mixed Student's t-test statistical method that detects high-scoring binding sites utilizing conservation information among species. The program performs sequence scanning at a rate of 3.2 Mbp/s on a quad-core Amazon server and has been benchmarked by the well-established ChIP-Seq datasets, putting Grit amongst the top-ranked TFBS predictors. It significantly outperforms the well-known transcription factor motif scanning tools, Pscan (4.8%) and FIMO (17.8%), in analyzing well-documented ChIP-Atlas human genome Chip-Seq datasets.

Grit is a good alternative to current available motif scanning tools.

Grit is a good alternative to current available motif scanning tools.

Based on the self-determination theory, the psychological requirements for competence, autonomy, and relatedness boost beneficial exercise behaviour for healthy living. However, there is no valid, reliable Malay version scale to investigate the extent to which these psychological needs are met. The main purpose of this study was to examine the psychometric properties of a Malay version of the Psychological Need Satisfaction in Exercise (PNSE-M) scale. In addition, the purpose of this study was to confirm the measurement and structural invariance of the PNSE-M across gender.

The study participants included 919 students (male 49.6%, female 50.4%), with a mean age of 20.4 years (standard deviation = 1.5). The participants were selected through convenience sampling. The 18-item PNSE-M was used to measure psychological need satisfaction in exercise. The English version of the PNSE was translated into Malay using standard forward-backward translation. Confirmatory factor analysis (CFA) and invariance tests were performed on the three domains of the PNSE-M model.